Modeling Phase Transitions in Gene Expression State Space

描述（由申请人提供）：生物学中成功的定量方法包括构建详细的局部模型或检测高通量数据中的稳健信号。在这个提议中，这两种方法以创新的方式结合起来，研究致癌病毒感染后人体组织中的转录变化。这些病毒可以产生一系列后果，从细胞表型的微小变化到急剧转变。从由关于病毒-宿主相互作用的所有已知信息组成的种子网络开始，将在基因表达数据上学习贝叶斯转录网络。然后将贝叶斯网络转换为相互作用的电磁自旋的等效系统。这种自旋系统的例子已经在统计物理学中进行了研究，并且已知它们具有丰富的相结构。对应于宿主细胞网络的自旋系统将被模拟，对齐的自旋域将被识别为表征细胞对扰动的响应的遗传模块。这些模块的激活水平将被用来划分基因表达状态空间中的阶段。新的相位和相变发现，以这种方式将通过实验验证。该框架从嘈杂的高吞吐量数据中筛选出概率交互，然后基于所得网络模型进行新颖的预测。这是一种新的，定量的，生物信息的方式来模拟人体细胞的扰动。在临床层面上，它可用于精细区分患者的各种正常和疾病状态，并计算哪些疗法最能逆转疾病的进展。这项技术有可能使医疗诊断和治疗更加有效、直接和精确。 公共卫生相关性：我的研究项目的目标是量化人类转录网络的扰动如何导致不同表型之间的转换。在这个框架下工作，临床医生将能够使用广泛可用的高通量方法检测疾病状态。然后，他们可以确定个性化治疗或治疗组合，这将最有效地逆转特定患者的疾病进展。

DESCRIPTION (provided by applicant): Successful quantitative approaches in biology have included building detailed local models or detecting robust signals in high-throughput data. In this proposal, both these methods are combined in an innovative way to study transcriptional changes in human tissue upon infection by oncogenic viruses. Such viruses can have a range of consequences, from minor changes to drastic transformations in the cell phenotype. Starting from a seed network consisting of all known information about the viral-host interaction, a Bayesian transcriptional network will be learned on the gene expression data. The Bayesian network is then transformed into an equivalent system of interacting electromagnetic spins. Examples of such spin systems have been studied in statistical physics, and they are known to have rich phase structures. The spin system corresponding to the host cell network will be simulated, and domains of aligned spins will be identified as genetic modules that characterize the response of the cell to perturbations. The activation levels of these modules will be used to demarcate phases in the gene expression state space. Novel phases and phase transitions discovered in this way will then be validated by experiments. This framework sifts out probabilistic interactions from noisy high- throughput data and then makes novel predictions based on the resulting network model. It is a new, quantitative, and biologically informative way to model perturbations to human cells. On a clinical level, it could be used to finely differentiate between various normal and disease states in patients, and to calculate which therapies would best reverse the progression of a disease. This technique has the potential to make medical diagnosis and treatment more efficient, directed and precise. PUBLIC HEALTH RELEVANCE: The goal of my research project is to quantify how perturbations to the human transcriptional network cause transitions between different phenotypes. Working in this framework, clinicians will be able to detect disease states using widely available high-throughput methods. They can then determine the personalized treatment, or combination of treatments, that will most efficiently reverse disease progression in a particular patient.